Introduction To Julia

An Interactive Tutorial

This introduction to the Julia programming language, and its uses in scientific computing, was given to the Pittsburgh Code and Supply meetup on January 12th, 2015. The presentation was given as an interactive tutorial, where everyone was encouraged to follow along in IJulia notebooks. To facilitate this, I spun up a pair of Jupyter notebook servers on Digital Ocean droplets that attendees could connect to and follow along without having to have Julia installed locally.

The presentation was given in two sections. The first section covered the basic syntax and semantics of the Julia language. The second section built upon that understanding and worked through an Monte Carlo simulation, highlighting some of the strengths of the Julia language in terms of its expressivity for numeric computing and its impressive performance.

There are no slides for this presentation, as it was given interactively. However, there is a GitHub repository with the code I put together for the presentation:

Additional Resources

Julia has a collection of learning resources on the official site, and their official documentation is very good. Additionally, I used Jupyter notebooks, which offer a interactive experience that is especially helpful when learning or doing exploratory programming.

For the second section of this presentation I also used two external packages to aid in plotting, Gadfly and DataFrame